OLS and 2SLS in Randomized and Conditionally Randomized Experiments
Ansel Jason (),
Hong Han () and
Jessie Li and ()
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Ansel Jason: GoDaddy.com, LLC, Kirkland, Washington
Hong Han: Department of Economics, Stanford University, Stanford, California
Jessie Li and: Department of Economics, University of California, Santa Cruz, California
Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), 2018, vol. 238, issue 3-4, 243-293
We investigate estimation and inference of the (local) average treatment effect parameter when a binary instrumental variable is generated by a randomized or conditionally randomized experiment. Under i.i.d. sampling, we show that adding covariates and their interactions with the instrument will weakly improve estimation precision of the (local) average treatment effect, but the robust OLS (2SLS) standard errors will no longer be valid. We provide an analytic correction that is easy to implement and demonstrate through Monte Carlo simulations and an empirical application the interacted estimator’s efficiency gains over the unadjusted estimator and the uninteracted covariate adjusted estimator. We also generalize our results to covariate adaptive randomization where the treatment assignment is not i.i.d., thus extending the recent contributions of Bugni, F., I.A. Canay, A.M. Shaikh (2017a), Inference Under Covariate-Adaptive Randomization. Working Paper and Bugni, F., I.A. Canay, A.M. Shaikh (2017b), Inference Under Covariate-Adaptive Randomization with Multiple Treatments. Working Paper to allow for the case of non-compliance.
Keywords: big data; data science (search for similar items in EconPapers)
JEL-codes: C1 C8 C9 (search for similar items in EconPapers)
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